System and method for monitoring manufacturing

ABSTRACT

A system for monitoring manufacturing includes one or more sensors and a controller in communication with the one or more sensors. The controller may include one or more processors that determine a quality metric represented by machine data collected from one or more machine data sensors and identify a correlation value between the machine data and environmental data collected from one or more environmental data sensors. The controller may further include determine if the correlation value exceeds a predetermined threshold value, and if the correlation value exceeds the predetermined threshold value, report at least one of the correlation value and the quality metric.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. § 119(e) of U.S.provisional application No. 62/264,718, titled “System and Method forMonitoring Manufacturing,” filed on Dec. 8, 2015, the content of whichis incorporated herein by reference in their entirety for all purposes.

BACKGROUND

Modern manufacturing factories generate a steady stream of complex,heterogeneous factory data collected from various types of sensorsdistributed throughout the manufacturing factories. Such data may be keyfor improving operations and product quality, and for addressingmanufacturing problems such as inefficiencies and underperformanceattributed to machine downtimes, supply chain interruptions, unfavorableambient conditions, among others. However, traditional approaches forusing the data are limited and cumbersome. For example, there can besignificant lag times between when a manufacturer is aware of a problemand when solutions are finally implemented. As another example, therecan be difficulties in tracing problems that are detected in a finalcompleted product to specific root causes among the many machines andprocesses. These difficulties are caused or exacerbated by challenges inextracting information and knowledge that may be hidden amongst diverseamount of factory data. Manufacturing problems, especially if identifiedtoo late, may require costly corrective measures, such as additionalproduct inspections, warranty claims and recalls, reworking products,and so on. Therefore, there is a need for a faster, more real-timeapproach to effectively utilizing the factory data and providingactionable solutions to manufacturers for improving their operations andproduct quality.

This application is intended to address such issues and to providerelated advantages.

SUMMARY

In general, the systems and methods disclosed herein are directed tomanufacturing, and more particularly, to manufacturing analytics.

In one aspect, the present disclosure provides for a system formonitoring manufacturing includes one or more sensors and a controllerin operative communication with the one or more sensors. The controllermay include one or more processors and a memory that is communicativelycoupled with and readable by the one or more processors. The memory mayhave stored thereon processor-readable instructions that, when executedby the one or more processors, cause the one or more processors todetermine a quality metric represented by machine data collected fromone or more machine data sensors and identify a correlation valuebetween the machine data and environmental data collected from one ormore environmental data sensors. The processor-readable instructions mayfurther cause the one or more processors to determine if the correlationvalue exceeds a predetermined threshold value, and if the correlationvalue exceeds the predetermined threshold value, report at least one ofthe correlation value and the quality metric.

Various embodiments of the present system may include one or more of thefollowing features. One or more sensors may include at least one machinedata sensor and at least one environmental data sensor. The system mayfurther include a server in operative communication with the controllerand/or in operative communication with one or more remote terminals. Theserver may include a cloud-based data server that has one or moredatabases, where the databases may store machine data and environmentaldata that is collected from the one or more sensors. Other examples arepossible.

In another aspect, the present disclosure provides for a method formonitoring manufacturing. The method may include determining a qualitymetric represented by machine data collected from one or more machinedata sensors and/or identifying a correlation value between the machinedata and environmental data collected from one or more environmentaldata sensors. The method may further include determining if thecorrelation value exceeds a predetermined threshold value, and if thecorrelation value exceeds the predetermined threshold value, reportingat least one of the correlation value and the quality metric.

Various embodiments of the present method may include one or more of thefollowing features. The method may include determining that the qualitymetric is indicative of a substandard quality and/or reporting thequality metric based on the determination. The method may includecomparing the machine data to at least one of an average value, a lowercontrol level value, and an upper control level value and/or determiningthe quality metric indicates the substandard quality based on thecomparison. The average value, the lower control level value, and theupper control level value may define a tolerance range for a part beingmanufactured and/or the substandard quality may represent the machinedata exceeds the tolerance range. The method may include receiving auser request for a root cause analysis based on the determination thatthe quality metric is indicative of the substandard quality and/oridentifying and reporting the correlation value in response to the userrequest. The method may include receiving a user request for at leastone of the correlation value and the quality metric and/or reporting atleast one of the correlation value and the quality metric in response tothe user request.

In another example feature, the method may include, based on thedetermination that the correlation value exceeds the predeterminedthreshold value, determining an environmental factor, where theenvironmental factor indicates at least one of a humidity reading,temperature reading, and pressure reading represented by theenvironmental data, and/or reporting the environmental factor. Themethod may include analyzing the environmental data and the machine datausing a regression analysis to identify the correlation value. Thepredetermined threshold value may include a minimum correlation factorthat is based on user input received during an initial setup procedure.The method may include retrieving data representing at least one of theenvironmental data and the machine data from a network database. Themethod may include determining a trend line having a plurality of pointsrepresenting the machine data over at least one of a period of time anda number of machine parts.

In yet another example feature, the method may include determining anaverage value for the machine data, determining if one or more of theplurality of points on the trend line cross the average value more thana predetermined number of times, and/or if the one or more points crossthe average value more than the predetermined number of times,initiating an alert message that the one or more points are fluctuatingabove or below the mean value. The method may include determining theone or more points exceed a tolerance range more than a predeterminednumber of times, where the tolerance range may be defined by a lowercontrol level value and an upper control level value, and based on thedetermination, generating an alert message indicating that the one ormore points exceed the tolerance range. The method may includedetermining an average value and a standard deviation based on themachine data, analyzing the machine data by applying one or more Nelsonrules and at least one of the average value and the standard deviation,determining an anomaly situation based on the analysis, where theanomaly situation indicates a violation event of the one or more Nelsonrules, and/or initiating an alert message indicating the determinedanomaly situation.

In still another example feature, the method may include determining anoutcome variable based on the correlation value, where the outcomevariable includes a variable type that is at least one of a categoricalvariable and an ordinal variable, and reporting the outcome variable.The variable type of the outcome variable may be based at least in parton a user request for a root cause analysis. The method may includemapping the machine data to one or more particular manufactured parts,and/or reporting the one or more particular manufactured parts alongwith at least one of the correlation value and the quality metric.Further, the method may include optimizing a monitored assembly linebased on the correlation value by determining one or more parallelsubassembly processes of the monitored assembly line and prioritizingthe one or more parallel subassembly processes in the monitored assemblyline based at least in part on the environmental data and the machinedata so that a production time length of the monitored assembly line isreduced. Other example features of the method may be contemplated.

In another aspect, the present disclosure provides for a system formonitoring manufacturing. The system may include one or more processorsand a memory communicatively coupled with and readable by the one ormore processors. The memory may have stored therein processor-readableinstructions that, when executed by the one or more processors, causethe one or more processors to determine a quality metric represented bymachine data collected from one or more machine data sensors and/oridentify a correlation value between the machine data and environmentaldata collected from one or more environmental data sensors. Theprocessor-readable instructions may cause the processor to determine ifthe correlation value exceeds a predetermined threshold value, and ifthe correlation value exceeds the predetermined threshold value, reportat least one of the correlation value and the quality metric. Otherexample features of the system may be contemplated, including one ormore of the various features described above in regard to the method.

In yet another aspect, the present disclosure provides for anon-transitory computer-readable medium storing one or more programs.The one or more programs may include instructions that, when executed byone or more processors of an electronic device, cause the electronicdevice to monitor manufacturing by determining a quality metricrepresented by machine data collected from one or more machine datasensors and/or identifying a correlation value between the machine dataand environmental data collected from one or more environmental datasensors. The instructions when executed cause the electronic device todetermine if the correlation value exceeds a predetermined thresholdvalue, and if the correlation value exceeds the predetermined thresholdvalue, report at least one of the correlation value and the qualitymetric. Other example features of the non-transitory computer-readablemedium may be contemplated, including one or more of the variousfeatures described above in regard to the method.

BRIEF DESCRIPTION OF THE FIGURES

The present application can be best understood by reference to thefollowing description taken in conjunction with the accompanying drawingfigures, in which like parts may be referred to by like numerals.

FIG. 1 shows a schematic diagram of a system for monitoringmanufacturing, according to various embodiments of the presentinvention;

FIG. 2 shows a method for monitoring manufacturing, according to variousembodiments of the present invention;

FIG. 3 shows another method for monitoring manufacturing, according tovarious embodiments of the present invention;

FIG. 4 shows yet another method for monitoring manufacturing, accordingto various embodiments of the present invention;

FIG. 5 shows a screenshot of machine data arranged in a tabular format,according to various embodiments of the present invention;

FIG. 6 shows a screenshot of machine data arranged in a timeline format,according to various embodiments of the present invention;

FIG. 7 shows a screenshot of machine data including images of acorresponding part or machine, according to various embodiments of thepresent invention;

FIG. 8 shows a screenshot of a dashboard view of overall equipmenteffectiveness, according to various embodiments of the presentinvention;

FIG. 9 shows a screenshot of machine data arranged in a chart view,according to various embodiments of the present invention;

FIG. 10 shows a screenshot of a root cause analysis tool, according tovarious embodiments of the present invention;

FIG. 11 shows a screenshot of a real-time statistical process controltool, according to various embodiments of the present invention;

FIG. 12 shows a screenshot of a message center, according to variousembodiments of the present invention;

FIG. 13 shows a screenshot of a user account configuration screen,according to various embodiments of the present invention; and

FIG. 14 shows a block diagram of a computing system upon which variousfeatures of the present disclosure may be provided.

It is noted that any of the elements and/or steps provided in the blockdiagrams, flow diagrams, method diagrams, and other illustrations of thefigures may be optional, replaced, and/or include additional components,such as combined and/or replaced with other elements and/or steps fromother figures and text provided herein. Various embodiments of thepresent invention are discussed below, and various combinations ormodifications thereof may be contemplated.

DETAILED DESCRIPTION

The following description is presented to enable a person of ordinaryskill in the art to make and use the various embodiments. Descriptionsof specific devices, techniques, and applications are provided only asexamples. Various modifications to the examples described herein will bereadily apparent to those of ordinary skill in the art, and the generalprinciples defined herein may be applied to other examples andapplications without departing from the spirit and scope of the presenttechnology. Thus, the disclosed technology is not intended to be limitedto the examples described herein and shown, but is to be accorded thescope consistent with the claims.

Techniques described herein are performed by software objects in someembodiments. For purposes of this disclosure, software objects may beinstantiated and resident in memory. In some embodimentscomputer-executable programs and corresponding instructions are providedto create and process software objects.

Manufacturers are concerned with high efficiency production processesand product quality. Important to addressing these concerns is thestream of factory data that is generated by manufacturing equipment,machines, and tools. The present systems and methods disclosed hereinprovide for an improved approach for monitoring manufacturing through anintegrated system that collects the factory data, analyzes the data, andpresents the data and analytics. In practice, the present systems andmethods may provide timely and actionable information that can beimplemented by the manufacturers for improving their productionprocesses and product quality. In doing so, the present systems andmethods may use the factory data to pinpoint manufacturing problems andtheir root causes in an efficient manner.

As described further in succeeding paragraphs, factory data, which mayalso be referred to herein as “parts and process data,” “manufacturingdata,” and “production data,” among other terminology, may be collectedfrom any number of varying sources throughout the manufacturing factory,such as sensors, cameras, text, barcodes, audio files, and laboratoryequipment. A challenge to using factory data as a tool for improvingmanufacturing lies within the inherent complexity of the raw data, whichmay be high in volume, highly varied, and produced at high velocity. Thepresent systems and methods overcome traditional limitations associatedwith using the factory data and provide a more robust approach formonitoring manufacturing. For instance, the present systems and methodsmay collect and interpret the data with context and meaning in real-timeand/or near real-time to provide actionable solutions quickly, inpractice, the present systems and methods may identify, prevent, and/orresolve manufacturing problems by identifying why an assembly line orsubassembly line is down, which may result, for example, from a lack ofmaterials, machine malfunction, missing personnel, and so on. Further,the present systems and methods may correct the problem, assess howquickly the problem is corrected, identify root causes of the problem,and identify trends for predicting future problems, it is contemplatedthat by quickly and precisely discovering and addressing underlyingproblems, manufacturers may improve their competitive position throughthe benefits associated with improved operations and product quality.

Turning to FIG. 1, an example system 100 for remotely monitoring amanufacturing factory 102 is shown. The manufacturing factory 102 mayinclude one or more controllers 104 in operative communication with oneor more sensors 106 a-g via a data network 108. The sensors 106 a-g maycollect data from a plurality of machines, product stages, assemblylines, and/or subassembly lines. For simplicity of illustration, FIG. 1shows the plurality of sensors 106 a-g monitoring a plurality ofmachines and/or product checkpoints, schematically represented asproducts 110 a-d, at a production assembly line 112 and an inspectionassembly line 114. It is noted that any number of products, productionstages, sensors, wireless and/or wired communication channels may becontemplated.

The controller 104 at the manufacturing factory 102 may be in uni- orbi-directional communication with a server 116 that includes and/orotherwise accesses one or more databases 118 a-c for storing the sensedfactory data, data analytics, activity logs, and so on. A remoteterminal 120 may be in operative communication with the manufacturingfactory 102 via the server 116. It is contemplated that one or moresteps of the monitoring manufacturing techniques described herein may beprovided by the controller 104 located at the manufacturing factory 102and/or the remote terminal 120, at the server 116, and/or anycombination thereof. It is noted that while remote monitoring is beingshown, one or more features of the present techniques may additionallyand/or alternatively be performed on-site. Further, additional and/oralternative communication channels, operating entities or elements,and/or interactions may be contemplated.

The sensors 106 a-g may include analog and/or digital sensors, such asbio sensors, chemistry and/or composition sensors, current and/or powersensors, air quality sensors, gas sensors, Hall Effect sensors,lightness level sensors, optical sensors, pressure sensors, temperaturesensors, ultrasonic sensors, proximity sensors, door status sensors,motion tracking sensors, humidity sensors, visible and infrared lightsensors, cameras, and so on. For example, a door status sensor maydetect an open and/or closed state of a door, in addition or alternativeto auto-opening and/or auto-locking of the door. Cameras may captureimages for visualizing and/or analyzing a particular factory and/ormanufactured part. Such sensors may collect data that is further usedindividually and/or in combination to determine various environmentalfactors, and/or assembly line operating statuses and/or conditions. Forexample, the sensor data may be utilized to determine if an assemblyline is shut down and/or operating properly.

As shown in FIG. 1, the sensors 106 a-g may send a signal to thecontroller 104 via data network 108, which may include one or two-waywireless communications and/or physical wiring channels, WiFi,Bluetooth, and/or other radio frequencies. In some cases, data collectedby the sensors may be stored at the databases 118-c provided on a cloud,such as a cloud server 116, that is accessible through the web andallows for remote data storage, backup, and/or processing. In someexamples, the controller 104 includes one or more programmable logiccontrollers (PLCs), software, and/or microprocessors, which may collect,process, analyze, and/or present the data according to various methodsdescribed herein. The controller 104 may trigger one or more flagsand/or notifications related to the monitored manufacturing, and/or canautomatically reconfigure one or more processes in an assembly lineaccording to one or more optimization rules. For example, based on thecollected sensor data, the controller 104 may not only report anunderperforming machine, but also automatically reconfigure one or morestages of the machine in an effort to reduce system downtime. In anotheraspect, automatically performing such adjustments may further increasesafety by eliminating a need for human interaction with the machine. Itis contemplated that any of the techniques disclosed herein may beperformed by one or more controllers 104 and/or at the server 116.

As mentioned above, server 116 may include a physical server and/or acloud server. In some examples, one or more databases 118 a-c are storedin a cloud server and include data collected from an assembly line,subassembly line, and/or may be modular representing each of amanufacturer's different assembly lines, factories, and/or factorylocations, locally and/or globally. In an exemplary embodiment, thesystem 100 collects the production data at the controller 104 and sendsthe collected data to the cloud server 116 which analyzes the data usingvarious machine learning algorithms, and other data conditioning andanalysis techniques, and presents the data through a graphical userinterface as described below. Analytics performed on the manufacturingdata may include transformations, calculations, and functions on rawdata using models representing manufacturing processes and parts. Suchmanufacturing analytics applications provide insight on, for example,part quality, process performance, OEE drill-down, root cause analysis,anomaly detection, traceability, real-time SPC, and predictivemaintenance, among others. In another aspect, the general manufacturercan map or otherwise correlate part data with certain parts, and/ormachine data with certain machines that manufactured certain parts. Forexample, if the general manufacturer identifies a machine problem with aparticular machine, the present systems and methods may identify whichparticular parts and/or overall products may have been equipped with thefaulty product. The cloud server 116 and/or the controller 104 mayprovide the data and analysis results to manufacturers through variousweb frameworks. Such web frameworks may include protocols that supportthe transfer of data using HTML, JavaScript, and/or JSON, so that theremote terminal 120 can display visualizations of the data through auser interface and update those visualizations as new data is computedby the server 116 and/or the controller 104.

Controller 104 and/or server 116 may also provide or include variousapplication programming interfaces (“APIs”) for storing the data intodatabases 118 a-c and other data management tools. APIs may aid furtherin retrieving data from the databases 118 a-c for various datamanagement systems, including software as a service (“SaaS”)applications that provide access to the database and/or can pull orotherwise retrieve data from the database and create metrics using suchdata. As noted above, such applications may be provided via web browsersto allow for remote operations management for the manufacturer. It iscontemplated that the system 100 may implement a plug-in architecturealong with the APIs for data acquisition to provide a plug and playconnection with the sensors and/or other data sources, such that uniquedata types, such as from homegrown manufacturing execution systems(“MES”) and/or custom factory, information technology (“IT”) systems,can be easily integrated.

Various types of communications channels between the entities shown inFIG. 1 may be contemplated, including uni-directional and/orbi-directional, wired and/or wireless communications. Merely by way ofexample, data network 108 may include an industrial protocol network,such as OPC, Modbus, ProfiNet, and the like. In other examples, any ofthe communication channels may be a dedicated conduit communication,such as a universal serial bus (USB), IEEE 802 (Ethernet), IEEE 1394(FireWire), or other high speed data communication standard. Otherexamples are possible.

Turning now to FIG. 2, an example method 200 for monitoringmanufacturing is shown. It is contemplated that one or more steps shownin the method 200, and/or any other methods disclosed herein, may beperformed by the controller 104, at the server 116, and/or a combinationthereof. Further, some steps disclosed herein may be rearranged,removed, and/or modified. Additional steps may be contemplated.

As shown in FIG. 2, the method 200 may include receiving a user requestfor a root cause analysis (step 202). For instance, the user request maybe initiated upon receiving a manufacturer's input that is directed todetermining an underlying cause for a machine down or an ascertainedpoor product quality level. The method 200 may include determining oneor mare quality metrics represented by machine data collected from oneor more machine data sensors (step 204), such as one or more of thesensors 106 a-g shown in FIG. 1. Such quality metrics may include,merely by way of example, a total number of defects, defect frequency,number of defects by shift, number of defects by type of defect, anddefect correlation to other recorded factors. Other quality metrics mayinclude, for example, a mean value and standard deviation calculatedbased on a current measurement received from the controller 104 and/orother measurements that were previously received from the controller104, comprehensive pass or fail results, rejection rate, total number offailures, substandard quality or other particular quality levels, andothers. In further aspects, a determined quality metric may includeresults generated by statistical processing including, for example,regression analysis, distribution analysis, or Nelson rules of processcontrol and other control charting techniques, as described furtherbelow. In a specific example, quality metrics may be determined toindicate a substandard quality based on one or more data pointsexceeding a tolerance range. Such tolerance ranges may be defined by anaverage value, lower control level value, and/or upper control levelvalue for a part being manufactured.

The method 200 may include identifying a correlation value between themachine data and environmental data that is collected from one or moreenvironmental sensors, which may include any example sensors 106 a-gdescribed previously (step 206). The method 200 may further includedetermining if a correlation value exceeds a predetermined thresholdvalue (step 208), which may be based on user input received during aninitial setup procedure. If the determination is positive, the method200 may include reporting the correlation value and/or the qualitymetric (step 210). If the correlation value does not exceed thepredetermined threshold value, the method 200 may include determining ifa user request had initiated the root cause analysis (step 212). If auser request had initiated the root cause analysis, the correlationvalue and/or the quality metric may be reported (step 210). Examples ofreporting may include generating text and/or e-mail messages, pop-upnotifications, logging the report in a spreadsheet or other database,and so on. If the correlation value does not exceed the predeterminedthreshold value, the method 200 may return to step 204 to continuedetermining one or more other, same or different, quality metrics. Inanother aspect, after reporting the correlation value and/or qualitymetric at step 210, the method 200 may loop back to step 204 tocontinuously analyze the factory data. Other examples and variations arepossible. Merely by way of example, method 200 may further includedetermining the predetermined threshold value based on availablemanufacturing data and/or receiving such values based on availablemanufacturing data and setting such values as the predeterminedthreshold value.

Turning to FIG. 3, another method 300 for monitoring manufacturing isshown. The method 300 may include receiving a user request for a qualitymetric (step 302), such as any of the quality metrics describedpreviously. The method 300 may include obtaining machine data and/orenvironmental data from a network-based database (step 304), such as oneor more of the databases 118 a-c hosted on or by the server 116. Themethod 300 may further include determining a quality metric that isrepresented by the machine data (step 306) and/or determining if thequality metric indicates that a manufactured part is within a tolerancerange (step 308). If the determination is within the tolerance range,the method 300 may include reporting the quality metric (step 310)and/or other information indicating that the manufactured part is withinthe tolerance range. If the quality metric indicates that themanufactured part is outside of tolerance, the method 300 may includeidentifying a correlation value for the machine data and environmentaldata (step 312), which may be acquired from the server 116. If thecorrelation value exceeds the predetermined threshold, the method 300may include determining an environmental factor (step 314), such as ahumidity, pressure, temperature, and/or lightness level, and reportingthe correlation value, environmental factor, and/or the quality metric(step 316).

Turning to FIG. 4, another example method 400 for monitoringmanufacturing is shown. The method 400 may include receiving a userrequest that includes a query (step 402) for a categorical and/orordinal state in regard to a particular type of machined part, machine,and/or assembly line or process. The method 400 may include determininga type of outcome variable based on the type of query (step 404), suchas a categorical outcome variable and/or an ordinal outcome variable.The method 400 may include determining a quality metric represented bymachine data that is collected from one or more machine data sensors(step 406). The method 400 may include, identifying a correlation valuebetween machine data and environmental data (step 408), which may beretrieved from a cloud-based data network such as the server 116 shownin FIG. 1. The method 400 may further include determining if thecorrelation value exceeds a predetermined threshold, and if so, mappingthe machine data to one or more particular machine parts (step 410).Further, the method 400 may include determining a categorical and/orordinal value of the outcome variable based on the correlation value,quality metric, and/or one or more particular manufactured parts (step412). The method 400 may further include reporting the outcome variable(step 414). Other examples are possible.

FIGS. 5-13 provide various example screenshots of a user interface thatmay be used and/or provided by the server 116 and/or controller. In someexamples, the various screenshots are provided as a part of amanufacturing monitoring application that is accessible to manufacturersthrough a web interface, web browser, and/or provided as a specialized,licensed software. While particular features of particular screenshotsare being shown, it is noted that any of the features shown in thefigures and described below may be interchanged, combined, optional,and/or located on multiple or other screenshots. Additional features maybe contemplated and added. Various combinations and examples arepossible.

Turning to FIG. 5, an example screenshot 500 of manufacturing datadisplayed in a tabular format by a user interface (UI) 502 is shown. TheUI 502 may include a plurality of tabs for presenting informationcollected from the sensors 106 a-g, such as an analysis tab 504, chartstab 506, data tab 508, timeline tab 510, and/or other tabs that may beadded. As shown in FIG. 5, the data tab 508 may display timing data fora plurality of machines in a tabular “spreadsheet” style that includesdata columns of individual machine identifiers or names 512, operatingstart time 514, operating end time 516, total length of operating time518, shift number 520, conveyor time 522, and additional rows andcolumns showing additional fields of data may be displayed by scrollinghorizontal and/or vertical scroll bars 524,526. An item count 528 maydisplay a total number of machines being monitored and/or having dataavailable for presentation in the data tab 508. A menu bar 530 may beprovided for various data types 532 to be filtered or displayed, such ascycle data, display format options 534, and/or particular machine typeoptions 536 which may be expanded for more options by a drop down menu.Such drop-down functionalities may narrow down the high volumes of dataaccessible by the scroll bars by grouping data according to sharedfeatures, such as cycle number and/or machine type.

The menu bar 530 may further include a time period selection 538 forview, such as the past 24 hours, past 7 days, past 30 days, past 90days, and so on. A work shift drop down box 540 may allow for selectionof a particular shift. Further, the UI 502 may provide a toolbar 542with an export option 544 to export the data to various file formats,including CSV and XLSX, a download option 546 for downloading dataand/or other files to be displayed in the UI 502, a delete option 548for erasing selected or highlighted data, and/or a print option 550 forprinting the data being displayed. A messages and notes section 552 maybe provided to record annotations from logged in users. In an example,the messages and notes section 552 includes rows pertaining toparticular machines such that each machine may be marked up with notes.The UI 502 may also implement one or more different colors for conveyinginformation, such as highlighting rows and/or columns, highlightingfields, coloring font, and so on. Merely by way of example, the UI 502may utilize red font and/or row shading for fields where tolerances areviolated. In another aspect, the UI 502 may present a UI settings 554,user statistics 556, and/or a user profile 558 which may be selected andexpanded to show further information and/or options.

FIG. 6 depicts an exemplary screenshot of manufacturing data viewed in atimeline format according to various embodiments. Data tab 600 providesa visual representation of machine state longitudinally, e.g., overviewtimeline 602, and at a per data-point level, e.g., cycle timeline 604.Overview timeline 602 may illustrate any sensed data or messages, suchas voltage 606, press temperature 608, and downtime messages 610 overtime. Overview timeline 602 is further navigable by sliding orrepositioning selection bar 612 to select a particular cycle ortimestamp for display in cycle timeline 604. Cycle timeline 604 mayprovide additional details for a selected timestamp. Values that moveoutside of tolerance may be shaded to indicate issues, as shown in presstemperature 608 of overview timeline 602. Overview timeline 602 may alsorepresent any time a machine is not operational, and/or programmablelogic controller errors or events, as shown in downtime messages 610graphs. Any of the overview timelines 602 may be collapsed and/orexpanded upon user selection of the timeline-specific icons 614. Cycletimeline 604 includes binary timings and analog values, as well as anytextual input for that cycle. The timeline format of FIG. 6 furtherprovides sidebar 616 to allow for browsing to specific events, such asparticular downtime events 618, messages 620, defects 622, and/or sensorreadings 624. Further, the timeline format may allow for filtering ofdata using filter toolbar 626 to narrow down data for display in theoverview timeline 602 and cycle timeline 604. As shown in FIG. 6, thetimeline format may be provided as a graphical user interface through aweb browser 628.

FIG. 7 depicts an exemplary screenshot of manufacturing data viewed interms of an image of a part or machine according to various embodiments.Data tab 700 allows for browsing of archived visual assets in adatabase, such as sidebar database 702, which allows for specific partsor cycles to be displayed. A plurality of images 704 corresponding to aparticular asset 706 selected from the sidebar database 702 may beassociated with either parts or machine cycles and selected from animages list 708. In practice, machine data may be mapped to one or moreparticular manufactured parts, and may be reported with one or moreother metrics, such as correlation values and/or quality metrics. Adiagram 710 of a selected vehicle or asset 706 may map where sensors712, such as cameras taking the images 704, are focused on a part ormachine. Image and other type of inspections may be highlighted with abanner 714 indicating a green (“pass”), red (“fail”), or gray (“N/A”).One or more screen border segments 716 a,b,c may be colored similarly toshow overall pass or fail state for a particular part or machine cycle.Further, a filter toolbar 718 may allow for browsing or filtering tospecific parts shown in the sidebar database 702 and/or the images list708.

FIG. 8 depicts an exemplary screenshot of a dashboard view of overallequipment effectiveness (“OEE”) according to various embodiments. A linegraph 800 showing performance levels of overall performance 802 a,availability 804 a, performance 806 a, and quality 808 a for a machineor machine type may be provided along with their corresponding values802 b, 804 b, 806 b, 808 b. A tabbed view of availability 810,performance 812, quality 814, output 816, and/or other subcomponents ofOEE may show drill down elements. For example, the availability tab 810illustrates a bar graph 818 for unplanned downtime by categoriesincluding part cropped, overheating, and machine error. Such interactivecharts allow drill down by day, machine, defect or downtime reason, andso on. Data shown in the OEE dashboard view may be selected for aparticular machine or time period, such as 7-day period, calendar week,last 30 days, calendar month, year-to-date (YTD), and so on, from one ormore drop-down menus provided on a filter toolbar 820. In some cases,the OEE view, along with any combination of other views, may be utilizedfor optimizing a monitored assembly line based on a correlation value.Such optimization may include determining one or more parallelsubassembly processes of the monitored assembly line and/or prioritizingthe one or more parallel subassembly processes in the monitored assemblyline in order to reduce production time length Other examples arepossible.

FIG. 9 depicts an exemplary screenshot of a chart view of manufacturingdata according to various embodiments. Chart view 900 allows simple datavisualization of raw and aggregated characteristics. A chart featuressidebar 902 may receive user selection for setting a x-axis data, y-axisdata, chart type, and/or provide a compare feature for comparing datafrom different parts or cycles. It is contemplated that individualreports may be linked to in the application and may be exported in PDFsor generated for e-mail reports. Machine/parts can be selectedindividually or in sets for comparison or aggregate metrics.

FIG. 10 depicts an exemplary screenshot of a root cause analysis toolaccording to various embodiments. The tool provides an analysisinterface 1000 that allows for explorations of correlations in rawmachine and/or part data. A regression model 1002 may be automaticallyselected based on inputs, e.g., categorical or continuous inputs, andoutputs, e.g., categorical, continuous, and/or events. Such inputs,and/or general initiation of a root cause analysis, may be selected orotherwise input by a user upon initiating one or more soft keys providedin the analysis interface 1000. In some cases, a regression analysis maybe used to analyze environmental data and machine data to identify acorrelation value. Machines and/or parts may be selected individually orin sets for comparison and/or aggregation. In some cases, as shown inFIG. 10, most statistically significant effects, such as those having Pvalue <0.05, are shown above a dotted line 1004 or other separator, andsorted by correlation coefficient 1006 in ascending or descending order.Relationship plot 1008 may illustrate relationships between variables,such as humidity and downtime probability, and a correspondingprobabilistic histogram 1010 may be provided.

FIG. 11 depicts an exemplary screenshot of a real-time statisticalprocess control tool according to various embodiments. The processcontrol tool 1100 may automatically and/or in response to userinstruction test new measurements against statistical processcontrol-based heuristics (“SPC heuristics”). Based on the testing,process control tool 1100 may provide a pop-up box alert 1102 indicatingany violations that cause the alert 1102 to be generated, a trend plot1104 showing a plurality of data points representing machine and/orenvironmental data, and/or a corresponding distribution histogram 1106showing measurements from a part and/or machine, such as a bearingsurface diameter. As shown in example FIG. 11, trend plot 1104 mayinclude one or more trend lines relative to an average level, lowerlevel, and/or upper level threshold. Such average level, lower level,and/or upper level threshold values may be determined by user inputand/or generated based on available machine data. In some examples, ifone or more of the plurality of points on the trend line crosses theaverage value more than a predetermined number of times, alert 1102 maybe instantiated indicating that one or more points are fluctuating aboveor below the mean value more than a predetermined number of times.Alerts 1102 may additionally and/or alternatively be dispersed as SMStext, e-mails, growl-style notifications, audio messages, and so on.

Other examples and variations are possible. For instance, users mayannotate points on the graph and/or exclude such points from beingplotted in the trendline. Options may exist for exporting the graphs toa PDF and/or combined in a report. Process control tool 1100 mayimplement a variety of SPC heuristics tests, such as Nelson's Rules ofStatistical Control, and may be configurable by a user, individualizedfor a user profile, and/or configured specifically per machine and/orpart being monitored. In another example, the tool 1100 may determine anaverage value and a standard deviation based on the collected machinedata, analyze the machine data by applying one or more Nelson rules,determine an anomaly situation that violates one or more of the Nelsonrules, and initiate an alert message indicating the determined anomaly.In still other examples, alert messages may be generated when one ormore points exceed a tolerance range more than a predetermined number oftimes, where the tolerance range is defined by a lower control levelvalue and an upper control level value. Such values may be user-defined.In still other examples, alerts 1102 may be indicative of anunderperforming machine, and/or cause the monitoring application toautomatically reconfigure one or more stages of the machine in an effortto reduce system downtime. Other examples are possible.

FIG. 12 depicts an exemplary screenshot of a message center 1200according to various embodiments. Alerts may be queued in an alerts list1202 and may be organized based on subject, type, machine, and/or datecolumns. Such alerts may be based on statistical process control, out oftrend events, and so on, and further may be shown as a “growl” stylealert for certain operating platforms, such as OS X platform. The alertslist 1202 may further show any outgoing messages and/or alerts and allowfor muting, e.g., muting as “nominal”, so they do not appear again. Uponselection of an alert from the alerts list 1202, further details, suchas graphs and descriptions of the alerts, may be provided in messagesdisplay 1204. Real-time alert notifications may be provided during thisinterface as pop-up box alerts 1206, which may also appear in the alertslist 1202.

FIG. 13 depicts an exemplary screenshot of a user configuration screenaccording to various embodiments. Configuration screen 1300 allowscreation and configuration of user accounts, as shown in user accountslist 1302, and/or management of customer-facing metadata. User accountsmay be designated for permission to add tolerances, part specs, machinespecs, manage downtime and/or defect codes, and/or configure shiftschedules, factory operation parameters, and so on. Other examples arepossible.

Turning now to FIG. 14, computer system 1400 which may be used toimplement the above-described embodiments is illustrated. In someembodiments, computer system 1400 includes one or more microprocessors1402 to execute computer-readable instructions; non-transitory computermemory 1406 to store computer-readable instructions, and disk storage1404 for storing data and computer-readable instructions; a display 1408for displaying system outputs; and an input peripheral 1410 (e.g.,keyboard, mouse, touch screen, and so on) to receive input from a user.The one or more microprocessors, memory, disk, display, and input unitsare connected by one or more bidirectional buses 1312 that transmit dataand/or computer-readable instructions between the units.

The computer system 1400 of FIG. 14 may be used, for example, toimplement any of the server 116, databases 118 a,b,c, and/or controllers104 of FIG. 1. Disk storage unit 1404 may be used to archive digitalimages or other sensed data received from one or more controllers 104 orsensors 106 a-g and/or store user interface application features anduser inputs. One or more microprocessors 1402 may be used for root causeanalysis calculations, to generate and communicate alerts, and toprovide other functionalities of the user interface. Memory 1406 may beused to store user interface software, statistical algorithms,computational results, queries, or other types of data orcomputer-readable instructions. Further, the computer system 1400 may beused to implement the remote terminal 120 of FIG. 1. In this case, theone or more microprocessors 1402 may be used to execute the userinterface that is displayed on display 1408. The display 1408 maydisplay the results of the root cause analysis, quality metrics, sensordata, system status, or other types of information related to the system100. Input peripheral 1410 may enable the user to enter new queries orto remotely update the controller software or sensor settings. Otherexamples are possible.

What is claimed is:
 1. A system for monitoring manufacturing,comprising: one or more sensors; and a controller in operativecommunication with the one or more sensors, wherein the controllercomprises one or more processors and a memory communicatively coupledwith and readable by the one or more processors and having storedtherein processor-readable instructions that, when executed by the oneor more processors, cause the one or more processors to: determine atrend line comprising a plurality of points representing machine datacollected from the one or more sensors over at least one of a period oftime or a number of machine parts, wherein the machine data collectedfrom the one or more sensors is associated with a manufacturing process;determine an average value for the machine data; determine if one ormore of the plurality of points on the trend line cross the averagevalue more than a predetermined number of times; if the one or morepoints cross the average value more than the predetermined number oftimes, initiate an alert message that the one or more points arefluctuating above or below the mean value; and reconfigure themanufacturing process based on the machine data collected from the oneor more sensors.
 2. The system of claim 1, wherein the one or moresensors comprise at least one machine data sensor and one environmentaldata sensor.
 3. The system of claim 1, further comprising a server inoperative communication with the controller and one or more remoteterminals.
 4. The system of claim 3, wherein the server comprises acloud-based data server having one or more databases, and wherein theone or more databases store the machine data and environmental datacollected from the one or more sensors.
 5. A non-transitorycomputer-readable storage medium storing one or more programs, the oneor more programs comprising instructions, which when executed by one ormore processors of an electronic device, instruct the electronic deviceto: determine a trend line comprising a plurality of points representingmachine data collected from one or more machine data sensors over atleast one of a period of time or a number of machine parts, wherein themachine data collected the one or more machine data sensors isassociated with a manufacturing process; determine an average value forthe machine data; determine if one or more of the plurality of points onthe trend line cross the average value more than a predetermined numberof times; if the one or more points cross the average value more thanthe predetermined number of times, initiate an alert message that theone or more points are fluctuating above or below the mean value; andreconfigure the manufacturing process based on machine data collectedfrom the one or more machine data sensors.
 6. The non-transitorycomputer-readable storage medium of claim 5, the one or more programsfurther comprising instructions, which when executed by the one or moreprocessors of the electronic device, instruct the electronic device to:determine that a quality metric represented by the machine data isindicative of a substandard quality; and report the quality metric basedon the determination.
 7. The non-transitory computer-readable storagemedium of claim 6, the one or more programs further comprisinginstructions, which when executed by the one or more processors of theelectronic device, instruct the electronic device to: compare themachine data to at least one of an average value, a lower control levelvalue, and an upper control level value; and determine the qualitymetric indicates the substandard quality based on the comparison.
 8. Thenon-transitory computer-readable storage medium of claim 7, wherein: theaverage value, the lower control level value, and the upper controllevel value define a tolerance range for a part being manufactured; andthe substandard quality represents the machine data exceeds thetolerance range.
 9. The non-transitory computer-readable storage mediumof claim 6, the one or more programs further comprising instructions,which when executed by the one or more processors of the electronicdevice, instruct the electronic device to: receive a user request for aroot cause analysis based on the determination that the quality metricis indicative of the substandard quality; and identify and report acorrelation value between the machine data and environmental datacollected from one or more environmental data sensors in response to theuser request.
 10. The non-transitory computer-readable storage medium ofclaim 5, the one or more programs further comprising instructions, whichwhen executed by the one or more processors of the electronic device,instruct the electronic device to: receive a user request for at leastone of a correlation value between the machine data and environmentaldata collected from one or more environmental data sensors and a qualitymetric represented by the machine data; and report at least one of thecorrelation value and the quality metric in response to the userrequest.
 11. The non-transitory computer-readable storage medium ofclaim 5, the one or more programs further comprising instructions, whichwhen executed by the one or more processors of the electronic device,instruct the electronic device to: based on a determination that acorrelation value between the machine data and environmental datacollected from one or more environmental data sensors exceeds thepredetermined threshold value, determine an environmental factor,wherein the environmental factor indicates at least one of a humidityreading, temperature reading, and pressure reading represented by theenvironmental data; and report the environmental factor.
 12. Thenon-transitory computer-readable storage medium of claim 5, the one ormore programs further comprising instructions, which when executed bythe one or more processors of the electronic device, instruct theelectronic device to analyze environmental data collected from one ormore environmental data sensors and the machine data using a regressionanalysis to identify a correlation value between the machine data andenvironmental data.
 13. The non-transitory computer-readable storagemedium of claim 5, wherein the predetermined threshold value is aminimum correlation factor that is based on user input received duringan initial setup procedure.
 14. The non-transitory computer-readablestorage medium of claim 5, the one or more programs further comprisinginstructions, which when executed by the one or more processors of theelectronic device, instruct the electronic device to retrieve datarepresenting at least one of environmental data collected from one ormore environmental data sensors and the machine data from a networkdatabase.
 15. The non-transitory computer-readable storage medium ofclaim 5, the one or more programs further comprising instructions, whichwhen executed by the one or more processors of the electronic device,instruct the electronic device to: determine the one or more pointsexceed a tolerance range more than a predetermined number of times,wherein the tolerance range is defined by a lower control level valueand an upper control level value; and based on the determination,generate an alert message indicating that the one or more points exceedthe tolerance range.
 16. The non-transitory computer-readable storagemedium of claim 5, the one or more programs further comprisinginstructions, which when executed by the one or more processors of theelectronic device, instruct the electronic device to: determine anaverage value and a standard deviation based on the machine data;analyze the machine data by applying one or more Nelson rules and atleast one of the average value and the standard deviation; determine ananomaly situation based on the analysis, wherein the anomaly situationindicates a violation event of the one or more Nelson rules; andinitiate an alert message indicating the determined anomaly situation.17. The non-transitory computer-readable storage medium of claim 5, theone or more programs further comprising instructions, which whenexecuted by the one or more processors of the electronic device,instruct the electronic device to: determine an outcome variable basedon a correlation value between the machine data and environmental datacollected from one or more environmental data sensors, wherein theoutcome variable comprises a variable type that is at least one of acategorical variable and an ordinal variable, wherein the variable typeof the outcome variable is based at least in part on a user request fora root cause analysis; and report the outcome variable.
 18. Thenon-transitory computer-readable storage medium of claim 5, the one ormore programs further comprising instructions, which when executed bythe one or more processors of the electronic device, instruct theelectronic device to: map the machine data to one or more particularmanufactured parts; and report the one or more particular manufacturedparts along with at least one of a correlation value between the machinedata and environmental data collected from one or more environmentaldata sensors and a quality metric represented by the machine data. 19.The non-transitory computer-readable storage medium of claim 5, the oneor more programs further comprising instructions, which when executed bythe one or more processors of the electronic device, instruct theelectronic device to: optimize a monitored assembly line based on acorrelation value between the machine data and environmental datacollected from one or more environmental data sensors by: determiningone or more parallel subassembly processes of the monitored assemblyline; and prioritizing the one or more parallel subassembly processes inthe monitored assembly line based at least in part on environmental datacollected from one or more environmental data sensors and the machinedata so that a production time length of the monitored assembly line isreduced.
 20. A computer-implemented method for monitoring manufacturing,the method comprising: determining a trend line comprising a pluralityof points representing machine data collected from one or more machinedata sensors over at least one of a period of time or a number ofmachine parts, wherein the machine data collected from the one or moremachine data sensors is associated with a manufacturing process;determining an average value for the machine data; determining if one ormore of the plurality of points on the trend line cross the averagevalue more than a predetermined number of times; if the one or morepoints cross the average value more than the predetermined number oftimes, initiating an alert message that the one or more points arefluctuating above or below the mean value; and reconfiguring themanufacturing process based on machine data collected from the one ormore machine data sensors.